GAVEL
发表于 2025-3-26 21:37:27
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adulterant
发表于 2025-3-27 01:57:22
On-Orbit Servicing Mission Planning for Multi-spacecraft Using CDPSO optimization (CDPSO) algorithm is applied according to the characteristics of multi-spacecraft collaborative mission planning problem. We design the new update formulae of position and velocity of the particles for the OOS optimization mission. By analyzing the critical index factors which contain
Toxoid-Vaccines
发表于 2025-3-27 05:31:39
Solving the Test Task Scheduling Problem with a Genetic Algorithm Based on the Scheme Choice Rulech combines a genetic algorithm with a new rule for scheme selection is adopted to find optimal solutions. GASCR is a hierarchal approach based on the characteristics of TTSP because the given problem can be decomposed into task sequence and scheme choice. GA with the non-Abelian (Nabel) crossover a
Petechiae
发表于 2025-3-27 11:08:50
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外来
发表于 2025-3-27 17:22:52
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ungainly
发表于 2025-3-27 20:35:17
Solving Flexible Job-Shop Scheduling Problem with Transfer Batches, Setup Times and Multiple Resourcducts. However, Flexible Job-shop Scheduling is really challenging and even more complex when setup times, transfer batches and multiple resources are added. In this paper, we present an application of dispatching algorithm for the Flexible Job-shop Scheduling Problem (FJSP) presented in this indust
Nonconformist
发表于 2025-3-27 23:46:48
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同时发生
发表于 2025-3-28 05:31:10
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Overstate
发表于 2025-3-28 08:17:07
An Improved Ensemble Extreme Learning Machine Based on ARPSO and Tournament-Selectionperformance and simple setting. However, how to select and cluster the candidate are still the most important issues. In this paper, KGA-ARPSOELM, an improved ensemble of ELMs based on K-means, tournament-selection and attractive and repulsive particle swarm optimization (ARPSO) strategy is proposed
进取心
发表于 2025-3-28 14:30:32
An Improved LMDS AlgorithmS (LMDS) is a fast algorithm of CMDS. In LMDS, some data points are designated as landmark points. When the intrinsic dimension of the landmark points is less than the intrinsic dimension of the data set, the embedding recovered by LMDS is not consistent with that of classical multidimensional scali